Slide 1 Functional Imaging: A review of fMRI, DTI and Non ...mri/seminars/slides/Advanced Neuro MRI...
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Slide 1 Functional Imaging: A review of fMRI, DTI and Non-invasive Perfusion Imaging Kristine Mosier DMD, Ph.D. Neuroradiology & Imaging Science Department of Radiology Clinical fMRI, Chief Head & Neck Imaging Associate Professor of Radiology, Neuroscience and Biomedical Engineering Indiana University School of Medicine & IUPUI ___________________________________ ___________________________________ ___________________________________ ___________________________________ ___________________________________ ___________________________________ ___________________________________ Slide 2 Overview Functional Brain Mapping Neurophysiology and hemodynamic basis of BOLD / CBF. fMRI paradigms, data acquistion and processing. Clinical case examples. Diffusion Tensor Imaging (DTI) & Fiber- tracking. Non-invasive Perfusion Imaging (Arterial Spin Labeling). Cases ___________________________________ ___________________________________ ___________________________________ ___________________________________ ___________________________________ ___________________________________ ___________________________________ Slide 3 ___________________________________ ___________________________________ ___________________________________ ___________________________________ ___________________________________ ___________________________________ ___________________________________
Transcript of Slide 1 Functional Imaging: A review of fMRI, DTI and Non ...mri/seminars/slides/Advanced Neuro MRI...
Slide 1 Functional Imaging: A review of
fMRI, DTI and Non-invasive Perfusion Imaging
Kristine Mosier DMD, Ph.D.Neuroradiology & Imaging Science
Department of RadiologyClinical fMRI, Chief Head & Neck Imaging
Associate Professor of Radiology, Neuroscience and Biomedical Engineering
Indiana University School of Medicine & IUPUI
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Slide 2 Overview
Functional Brain Mapping Neurophysiology and hemodynamic basis of BOLD
/ CBF.
fMRI paradigms, data acquistion and processing.
Clinical case examples.
Diffusion Tensor Imaging (DTI) & Fiber-tracking.
Non-invasive Perfusion Imaging (Arterial Spin Labeling).
Cases
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Slide 3
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Slide 4 Functional Imaging: fMRI
Brain activity can be mapped using either BOLD technique (Blood Oxygen Level Dependent) or • rCBF.
Both BOLD and CBF changes dependent on neurovascular coupling.
BOLD signal most closely correlated with LFP (local field potentials).
fMRI performed in the neurosurgical setting to map eloquent cortex.
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Slide 5
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Slide 6 fMRI
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Slide 7 BOLD mechanism: summary
Neuronal activity focal net increase in blood flow and oxygenation.
Increase in focal oxygenated blood decrease in deoxyhemoglobin less T2* effect increase in signal intensity.
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Slide 9
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Slide 10 BOLD fMRI: contrast mechanism
Relative mismatch between O2
delivery and O2 extraction during activation period
Blood flow is increased to activated regions of brain
O2 extraction also increased, but less than increase in O2 delivery
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Slide 11 BOLD fMRI: contrast mechanism
Thus increased oxyHb at post-capillary level decreased deoxyHb
DeoxyHb is paramagnetic
decreases T2* (decreases signal)
Decrease in local deoxyHb results in increased signal intensity on T2*-wtd images (• 1-5%)
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Slide 12 Basis of BOLD fMRI
From: Moseley ME & Glover GH. NeuroImaging Clinics of North America; Functional Neuroimaging 5(2): 161-191, 1995
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Slide 13 Basis of BOLD fMRI
From: Moseley ME & Glover GH. NeuroImaging Clinics of North America; Functional Neuroimaging 5(2): 161-191, 1995
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Slide 14 Raw Image Time Series
visual stim
visual stim no stim
no stim
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Slide 15 Difference Image Time Series
visual stim
visual stim no stim
no stim
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Slide 16 • Peak BOLD signal arises at the level of the post-capillary venule .
•Problem: contribution to signal from draining veins • spatial, temporal artifact.
• Animal expt. at high field (e.g. 7-9T) within 200 µm of LFP.
• Humans: several studies BOLD accurate to w/in 1cm of electrode.
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Slide 17
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Slide 18
FFT
Echo Planar Imaging
k-space image space
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Slide 19 fMRI Data acquistion
Acquire time-series of fast images while subject performs sensorimotor, language or cognitive task
Process time-series data using statistical methods & compare signal change during task performance with signal during rest / baseline.
Accurate processing: need to remove drift, motion, physiological noise.
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Slide 20 fMRI : Study Overview
Patient Preparation
Paradigm Design
Data Acquisition
Image Reconstruction and Processing
Statistical Maps Computation
Visualization of Maps and Analysis
Data Transfer
Workstation
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Slide 21
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Slide 22
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Slide 23 fMRI Data: Statistical Analysis Most commercially available and custom
algorithms use GLM (General Linear Model).
Y = M*a+e; Y = data vector, M = model of amplitude response, a = response amplitude, e = noise.
Current scanner software (GE, Siemens) has “real-time” processing using t-test: BEWARE; not all noise & artifact removed!
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Slide 24 fMRI Post-Procesing: FT paradigmRaw EPI
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Slide 25 fMRI Post-Procesing: FT paradigmBas_MoCo
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Slide 26 Post-Processing: AFNI#!/bin/bash# set HOST = `hostname`echo "subj=080821_HPA"read subjecho $subjstadir=/data6/${subj}refpath=/home/yang/fmriref#TR=3s#nslices=32#frame=144##tasks=(FT RB LB VS RM WG NA)tasks=(BWchbd CLchbd)#frames=(144 144 144 144 144)##datadir=(8-fMRI-EPI-FT 22-fMRI-EPI-RB 15-fMRI-EPI-LB 29-fMRI-EPI-WG 36-fMRI-EPI-RM 43-fMRI-EPI-NA)datadir=(10-ep2d_pace_stamp_BA 14-ep2d_pace_stamp_WG 16-ep2d_pace_stamp_NA 18-ep2d_pace_stamp_RM 20-ep2d_pace_stamp_PL 26-ep2d_pace_stamp_CS)cd ${stadir}/afni## segmentation mprage in SPM5 and generate brain only mask on segemented brain## coregister T2 or T1 to mprage in SPM5 and then coregister EPI-MC.nii to rT2.nii or rT1.nii## coregister *MC.nii in SPM5 --- estimate ONLY##first convert these spm_output_files back to afni#files=`ls r*MC*.nii`#for file in ${files}#do#echo run for ${file}# origfile=${file#r}# \rm tmp*# 3dresample -dxyz 2.5 2.5 3.5 -prefix tmp-rs -inset ${file}# 3dresample -master tmp-rs+orig -inset ${origfile} -prefix tmp-orig-rs# 3dWarpDrive -affine_general -base tmp-rs+orig -prefix ${origfile%.nii}-fix tmp-orig-rs+orig#done#files=`ls rMask*.nii`#for file in ${files}#do#echo run for ${file}# origfile=${file#r}# \rm tmp*# 3dresample -dxyz 2.5 2.5 3.5 -prefix tmp-rs -inset ${file}# 3dresample -master tmp-rs+orig -inset ${origfile} -prefix tmp-orig-rs# 3dWarpDrive -affine_general -base tmp-rs+orig -prefix ${origfile%.nii}-fix tmp-orig-rs+orig# ###3dresample -master ${origfile} -inset ${file} -prefix tmp-rs# ###3dWarpDrive -affine_general -base tmp-rs+orig -prefix ${origfile%.nii}-fix -twopass ${origfile}#done## now analyze each task#echo all tasks are ${tasks[*]}n=0for task in ${tasks[*]}do
echo run for $task3dvolreg -verbose -Fourier -prefix ${subj}-${task}-MC.nii -base 0 -zpad 4 -tshift 0 -1Dfile ${subj}-${task}mc ${task}.nii1dplot -ps -volreg -one -nopush -xlabel ${subj}-${task} ${subj}-${task}mc > ${subj}-${task}mc.ps3dNotes -HH " " ${subj}-${task}-MC.nii.gz3dAutomask -prefix Mask-${task} ${subj}-${task}-MC.nii.gz3dDespike -prefix ${subj}-${task}-MCds ${subj}-${task}-MC.nii.gz3dmerge -1blur_fwhm 5 -doall -prefix ${subj}-${task}-MCdssm ${subj}-${task}-MCds+origfile=${subj}-${task}-MCdssm+origcp ${subj}-${task}mc mc3dDeconvolve -input ${file} \
-progress 10000 \-mask Mask-${task}+orig \-polort A -num_stimts 8 \-stim_file 1 ${refpath}/test_on.wav.1D -stim_label 1 'On' \-stim_file 2 ${refpath}/test_off.wav.1D -stim_label 2 'Off' \-stim_file 3 'mc[0]' -stim_base 2 -stim_label 3 Roll \-stim_file 4 'mc[1]' -stim_base 3 -stim_label 4 Pitch \-stim_file 5 'mc[2]' -stim_base 4 -stim_label 5 Yaw \-stim_file 6 'mc[3]' -stim_base 5 -stim_label 6 dS \-stim_file 7 'mc[4]' -stim_base 6 -stim_label 7 dL \-stim_file 8 'mc[5]' -stim_base 7 -stim_label 8 dP \-num_glt 4 \-glt_label 1 'On' -gltsym 'SYM: +On' \-glt_label 2 'Off' -gltsym 'SYM: +Off' \-glt_label 3 'On-Off' -gltsym 'SYM: +On -Off' \-glt_label 4 'Off-On' -gltsym 'SYM: +Off -On' \-bucket ${subj}-${task}+mlr -nocout -tout -fout -rout -vout \-fitts ${subj}-${task}+fit -errts ${subj}-${task}+ert -xsave \-cbucket ${subj}-${task}+cbk -x1D ${task}
3drefit -addFDR ${subj}-${task}+mlr+orig#3dFDR -input ${subj}-${task}+mlr+orig -mask_file Mask-${task}-fix+orig -prefix ${subj}-${task}+mlrFDR#3dNotes -HH " " ${subj}-${task}+mlrFDR+orig((n=n+1))
done
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Slide 27 fMRI Post-Procesing: FT paradigm
Motion Parameters
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Slide 28 fMRI Post-Procesing: FT paradigm
<matrix# ni_type = "11*double"# ni_dimen = "144"# ColumnLabels = "Run#1Pol#0 ; Run#1Pol#1 ; Run#1Pol#2 ; On#0 ; Off#0 ; Roll#0 ; Pitch#0 ; Yaw#0 ; dS#0 ; dL#0 ; dP#0"# ColumnGroups = "3@-1,1,6@0,8"# RowTR = "2"# GoodList = "0..143"# NRowFull = "144"# RunStart = "0"# Nstim = "2"# StimBots = "3,10"# StimTops = "3,10"# StimLabels = "On ; dP"# Nglt = "4"# GltLabels = "On ; Off ; On-Off ; Off-On"# GltMatrix_000000 = "1,11,3@0,1,7@0"# GltMatrix_000001 = "1,11,4@0,1,6@0"# GltMatrix_000002 = "1,11,3@0,1,-1,6@0"# GltMatrix_000003 = "1,11,3@0,-1,1,6@0"# CommandLine = "3dDeconvolve -input 100712OOS-FT-dsMCewsm+orig -progress 30000 -automask -float -polort A -num_stimts 8 -stim_file 1 /home/yang/doc/fmriref/p4on5off_on_wav.1D -stim_label 1 On -stim_file 2 /home/yang/doc/fmriref/p4on5off_off_wav.1D -stim_label 2 Off -stim_file 3 'mc[0]' -stim_base 2 -stim_label 3 Roll -stim_file 4 'mc[1]' -stim_base 3 -stim_label 4 Pitch -stim_file 5 'mc[2]' -stim_base 4 -stim_label 5 Yaw -stim_file 6 'mc[3]' -stim_base 5 -stim_label 6 dS -stim_file 7 'mc[4]' -stim_base 6 -stim_label 7 dL -stim_file 8 'mc[5]' -stim_base 7 -stim_label 8 dP -num_glt 4 -glt_label 1 On -gltsym 'SYM: +On' -glt_label 2 Off -gltsym 'SYM: +Off' -glt_label 3 On-Off -gltsym 'SYM: +On -Off' -glt_label 4 Off-On -gltsym 'SYM: +Off -On' -bucket 100712OOS-FT+mlr -nocout -tout -fout -rout -vout -fitts 100712OOS-FT+fit -errts 100712OOS-FT+ert -xsave -cbucket 100712OOS-FT+cbk -x1D FT"# >
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Slide 29 fMRI Post-Procesing: FT paradigmIntermediate T-map
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Slide 30 fMRI Post-Procesing: FT paradigmDesign
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Slide 31 fMRI Post-Procesing: FT paradigmMean + T-map
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Slide 32 fMRI Post-Procesing: FT paradigmGLM
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Slide 33 fMRI: Typical Tasks
Sensorimotor Gross motor: Finger-tapping, tongue tapping
Fine motor: object manipulation (Mosier)
Sensory: Visual field / retinotopic mapping
Language: expressive & receptive speech
Expressive speech: Word generation, Object naming, Rhyming
Receptive speech: Passive Listening, Rhyming
Memory: Working memory
Other: Swallowing / articulated speech (Mosier)
Not yet standardized: ASFNR working on that!
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Slide 34 fMRI: Choice of Tasks
Tasks
Location Gross Motor
Fine Motor
Language VisualField
WorkingMemory
Other
Frontal + + /- +
Parietal + + + +
Temporal + / - + + /-
Occipital + Object Naming
+
Insular + + + +
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Slide 35 fMRI: Patient Selection Intracranial lesion requiring eloquent cortex
mapping.
Patient able to undergo MR imaging at 1.5T or 3T (only 3T at IUPUI). AVM patients: specific clips not safe @ 3T.
Stents; not all safe @ 3T
Body habitus
Claustrophobia
Able to speak & understand English
Able to read @ 6th grade level.
Peds + /-
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Slide 36
A
fMRI(Language rhyming task )
ESC
Clinical fMRI: Neurosurgical Mapping
IU Radiology fMRI
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Slide 37 Case 1: Oligodendroglioma; Bilateral Finger tapping
Central sulcus
Pre-central gyrus
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Slide 38 Case 1 Oligodendroglioma; Object manipulation –right hand: fine motor, sensory – tactile, proprioception
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Slide 39 Case 1: Oligodendroglioma Language: Word Generation: speech execution
Activation
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Slide 40 Case 1: Oligodendroglioma Language: Naming; semantic language: speech reception and
execution
Noise Activation
Noise
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Slide 41 Case 1: Oligodendroglioma Language: Rhyming; semantic language
Noise
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Slide 42 Case 2: Finger-tapping
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Slide 43 Case 2: Object Manipulation
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Slide 44 Case 2: Working Memory
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Slide 45 fMRI Brain MappingAdvantages: Non-invasive mapping of eloquent cortex w/
maps co-registered to anatomical images. In many institutions, this has completely replaced
WADA testing.
Disadvantages: Not all subjects are candidates: MRI safety,
patient must be awake & cooperative, peds. Requires a team with expertise: neuroradiology,
neurosurgery, neuropsych., MR physicists, image processing specialists, etc.
Indirect measure of neuronal activity.
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Slide 46 Case 3:WHO Grade II oligoastrocytoma
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Slide 47 FT TT
Naming Rhyming
Case 3:WHO Grade II oligoastrocytoma
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Slide 48 Case 3:WHO Grade II oligoastrocytoma
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Slide 49 Case 4: Visual Cortex, Awake
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Slide 50 Case 4: Visual Cortex, Awake
Note: patient has granted permission for his photos to be used for publication and teaching
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Slide 51 Case 4: Visual Cortex, Awake
Note: patient has granted permission for his photos to be used for publication and teaching
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Slide 52 Case 4:Oligoastrocytoma < Gr IIILeft Chkbd
Right Chkbd
Face Matching
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Slide 56
Fig. 9.3
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Slide 57 MR Diffusion Tensor Imaging
In liquids In tissues Anisotropy
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Slide 58
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Slide 59
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Slide 60 DTI
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Slide 61
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Slide 62
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Slide 63 Case 5 - DTI/FA
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Slide 64
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Slide 65
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Slide 66 DTI: Fiber Tracking
Adapted from: Descoteaux et al. 2007
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Slide 67 DTI at 3T
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Slide 68 Case 1 DTI
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Slide 69 Case 2: DTI
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Slide 70 A B
C D
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Slide 72
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Slide 73 Case 6: 54 y.o. w/partial complex seizures & speech arrest
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Slide 74 Case 6: Bilateral Finger-tapping
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Slide 75 Case 6: Word Generation
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Slide 76 Case 6: Naming
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Slide 77 Case 6: DTI
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Slide 78 Case 6: Perfusion (ASL)
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Slide 79
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